Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …

Sprocket is a highly configurable, stage-based, scalable, serverless video processing
framework that exploits intra-video parallelism to achieve low latency. Sprocket enables …

Small jobs, that are typically run for interactive data analyses in datacenters, continue to be
plagued by disproportionately long-running tasks called stragglers. In the production …

We present an automatic skew mitigation approach for user-defined MapReduce programs
and present SkewTune, a system that implements this approach as a drop-in replacement …

Hawk: Hybrid Datacenter Scheduling Page 1 This paper is included in the Proceedings of
the 2015 USENIX Annual Technical Conference (USENIC ATC ’15). July 8–10, 2015 • …

MapReduce is a widely used parallel computing framework for large scale data processing.
The two major performance metrics in MapReduce are job execution time and cluster …

Because of the vast availability of data, there has been an additional focus on the health
industry and an increasing number of studies that aim to leverage the data to improve …

In big data analytics, timely results, even if based on only part of the data, are often good
enough. For this reason, approximation jobs, which have deadline or error bounds and …

MapReduce offers a promising programming model for big data processing. Inspired by
functional languages, MapReduce allows programmers to write functional-style code which …

Serverless platforms facilitate transparent resource elasticity and fine-grained billing, making
them an attractive choice for data analytics. We find that while server-centric analytics …